arXiv:2409.18142v1 Announce Type: new
Abstract: The rapid evolution of Multimodal Large Language Models (MLLMs) has brought substantial advancements in artificial intelligence, significantly enhancing the capability to understand and generate multimodal content. While prior studies have largely concentrated on model architectures and training methodologies, a thorough analysis of the benchmarks used for evaluating these models remains underexplored. This survey addresses this gap by systematically reviewing 211 benchmarks that assess MLLMs across four core domains: understanding, reasoning, generation, and application. We provide a detailed analysis of task designs, evaluation metrics, and dataset constructions, across diverse modalities. We hope that this survey will contribute to the ongoing advancement of MLLM research by offering a comprehensive overview of benchmarking practices and identifying promising directions for future work. An associated GitHub repository collecting the latest papers is available.
The Significance of Multimodal Large Language Models (MLLMs)
Over the years, Multimodal Large Language Models (MLLMs) have witnessed rapid evolution, revolutionizing the field of artificial intelligence. These models have significantly enhanced our capability to understand and generate multimodal content, which has numerous practical applications across various industries. However, while researchers have focused primarily on model architectures and training methodologies, the benchmarks used to evaluate these models have received limited attention.
This survey aims to bridge this gap by systematically reviewing 211 benchmarks that assess MLLMs across four fundamental domains: understanding, reasoning, generation, and application. By diving deep into the task designs, evaluation metrics, and dataset constructions, the survey sheds light on the intricacies of evaluating MLLMs across diverse modalities.
The Multi-Disciplinary Nature of MLLM Research
One of the key takeaways from this survey is the multi-disciplinary nature of MLLM research. Due to the complex nature of multimodal content, effectively evaluating MLLMs requires expertise from various fields. Linguists, computer scientists, psychologists, and domain experts from different industries must collaborate to construct meaningful benchmarks that capture the richness and complexity of multimodal data.
These benchmarks are not limited to a single modality; instead, they encompass a wide range of input types, including text, images, videos, and audio. The diverse nature of the benchmarks ensures that MLLMs are tested against real-world scenarios, where multimodal content is inherently entangled, requiring the models to understand and generate content in a coherent and meaningful manner.
Identifying Promising Directions for Future Work
By analyzing the current benchmarking practices, this survey also identifies several promising directions for future MLLM research. One notable area is the development of more comprehensive and challenging benchmarks that can better evaluate MLLMs’ capabilities. These benchmarks should strive to capture the nuances and context-dependent nature of multimodal content, providing opportunities for innovative research and development of MLLMs.
In addition, the survey emphasizes the importance of standardized evaluation metrics and guidelines for benchmarking MLLMs. This standardization would enable fair comparisons between different models and facilitate progress in the field. Researchers should work towards consensus on evaluation metrics, considering factors such as objectivity, interpretability, and alignment with human judgment.
The associated GitHub repository, which collects the latest papers in the field, serves as a valuable resource for researchers and practitioners seeking to stay updated on the advancements in MLLM research.
Conclusion
This survey provides a comprehensive overview of benchmarking practices for Multimodal Large Language Models (MLLMs). It highlights the multi-disciplinary nature of MLLM research, which requires collaboration between experts from various fields. The survey also identifies promising directions for future work, emphasizing the need for more challenging benchmarks and standardized evaluation metrics. By addressing these considerations, researchers can further advance the capabilities of MLLMs and unlock their potential in understanding and generating multimodal content.
Keywords: Multimodal Large Language Models, MLLMs, benchmarking practices, evaluation metrics, multimodal content.
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